Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics.
By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.
By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.The homeschooling timetable blanket is a visualization of the 8 weeks that I spent supporting my 13-year-old son with home learning during January and February 2021. Each multi-colored crochet "granny" square represents 1 day of schooling. The red squares are weekend days, and the lime green ones are half-term holidays. Each treble crochet row within the multi-colored squares represents a 1 h lesson and is color coded per subject, e.g., red for science, yellow for history, burnt umber for PE, and so on.Drawing from a study of archaeological excavation teams, four collective curation opportunities are proposed to identify and resolve differences in data and documentation practices that arise in team-based research. To create more integrated, well-documented data, the opportunities attend to integrating people rather than technology. The actions people take as data move through the life cycle become the focal point of change.Machine learning has become a standard tool for medical researchers attempting to model disease in various ways, including building models to predict response to medications, classifying disease subtypes, and discovering new therapies. In this preview, we review a paper that utilizes quantum computation in order to tackle a critical issue that exists with medical datasets they are small, in that they contain few samples. The authors' work demonstrates the possibility that these quantum-based methods may provide an advantage for small datasets and thus have a real impact for medical researchers in the future.Babur et al. (2021) developed the CausalPath tool to infer causal signaling interactions in high-throughput proteomics data that may foster mechanical understanding from large-scale biological datasets.Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https//github.com/caanene1/ACSNI).Three dissimilar methodologies in the field of artificial intelligence (AI) appear to be following a common path toward biological authenticity. This trend could be expedited by using a common tool, artificial nervous systems (ANS), for recreating the biology underpinning all three. ANS would then represent a new paradigm for AI with application to many related fields.The shift of attention from the decline of organized religion to the rise of post-Christian spiritualities, anti-religious positions, secularity, and religious indifference has coincided with the deconstruction of the binary distinction between "religion" and "non-religion"-initiated by spirituality studies throughout the 1980s and recently resumed by the emerging field of non-religion studies. The current state of cross-national surveys makes it difficult to address the new theoretical concerns due to (1) lack of theoretically relevant variables, (2) lack of longitudinal data to track historical changes in non-religious positions, and (3) difficulties in accessing small and/or hardly reachable sub-populations of religious nones. We explore how user profiling, text analytics, automatic image classification, and various research designs based on the integration of survey methods and big data can address these issues as well as shape non-religion studies, promote its institutionalization, stimulate interdisciplinary cooperation, and improve the understanding of non-religion by redefining current methodological practices.One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.The transition of energy grids toward future smart grids is challenging in every way politically, economically, legally, and technically. While many aspects progress at a velocity unthinkable a generation ago, one aspect remained mostly dormant human electricity consumers. The involvement of consumers thus far can be summarized by two questions "Should I buy the eco-friendly appliance? Will solar pay off for me?" However, social and psychological aspects of consumers can profoundly contribute to resilient smart grids. This vision paper explores the role of active consumer-producers (prosumers) in the resilient operation of smart energy grids. We investigate how data can empower people to become more involved in energy grid operations, the potential of heightened awareness, mechanisms for incentives, and other tools for enhancing prosumer actions toward resilience. We further explore the potential benefits to people and system when people are active, aware participants in the goals and operation of the system.We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http//causalpath.org.The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.The maturity of the computational argumentation field, demonstrated with the first live debate between a machine and a human,1 triggers a demanding question how can we build argumentation technologies that bring people together? We believe that an important part of the answer is to include the audience's beliefs into the process.Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. https://www.selleckchem.com/products/curzerene.html Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.Microglia are important immune cells in the central nervous system. Replacement of mutated microglia by wild-type cells through microglia replacement by bone marrow transplantation can correct gene deficiencies. However, the limited availability of bone marrow cells may restrict its potential of becoming a widely used clinical treatment. Here, we introduce a potentially clinical-feasible strategy achieving efficient microglia replacement by peripheral blood cells in mice, boosting the donor cell availability. We named it microglia replacement by peripheral blood (Mr PB). For complete details on the use and execution of this protocol, please refer to Xu et al. (2020). The original abbreviation of this microglia replacement strategy is mrPB. We hereby change the name to Mr PB.Reproducible in vivo models are necessary to address functional aspects of the gut microbiome in various diseases. Here, we present a gnotobiotic mouse model that allows for the investigation of specific microbial functions within the microbiome. We describe how to culture 14 different well-characterized human gut species and how to verify their proper colonization in germ-free mice. This protocol can be modified to add or remove certain species of interest to investigate microbial mechanistic details in various disease models. For complete details on the use and execution of this protocol, please refer to Desai et al. (2016).
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